Abstract WP131: Global Landscape of Stroke Rehabilitation: Access and Challenges
Bibliographic record
Abstract
Background: Stroke is a leading cause of long-term disability worldwide. Timely and adequate rehabilitation is crucial for post-stroke recovery, but access is limited due to an overburdened healthcare system, medical personnel shortages, and insurance barriers. Rehabilitation access is especially limited for stroke survivors in rural and low-income regions. Innovative solutions like telerehabilitation are needed to expand access. This study examines current rehabilitation practices, costs, and global telerehabilitation models, as well as barriers to rehabilitation utilization. Methods: We conducted an online survey aimed at stroke care providers, affiliated societies, and partner organizations to collect comprehensive data on the availability and practices of post-stroke rehabilitation and telerehabilitation across various regions. Results: A total of 523 responses were collected from 62 different countries (Fig1), with the majority of respondents being physicians (66.7%), followed by physiotherapists (15.7%). Most respondents reported working in urban areas (82.9%) and were primarily employed in public community hospitals (40.5%), with a significant portion also working in academic institutions (35.3%). Regarding experience, 45.1% of respondents had over 10 years of experience in the stroke field. Telerehabilitation services for stroke were not offered by most of the surveyed sites (71.1%). Among those that did provide telerehabilitation, most sessions were individualized for a single patient (34.7%), followed by sessions involving two patients (24.8%). Notably, 18.36% of the sites offered sessions for groups of more than 10 patients at a time. The frequency of sessions varied, with the majority offering a single session per week (30.6%), followed by two sessions per week (20%), while only 8% provided sessions five days a week. On average, the duration of these sessions ranged from 31 to 60 minutes (53.1%). The most reported barriers to providing adequate telerehabilitation services included the availability of electronic hardware devices (12.9%) and internet access (12.9%). Other significant barriers included poor video call quality (10.4%) and the lack of clear guidelines and protocols (9.2%). Conclusion: The survey results provide a comprehensive overview of current practices and availability of post-stroke rehabilitation and telerehabilitation, highlighting the global burden of post-stroke disability due to limited access to rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".